Objectives  Ship detection in large-scale, high-resolution optical remote sensing images, especially in visible spectral remote sensing images, plays an important role in both military and civilian fields. It has always been an on-going and challenging research topic in the past decades, due to the complexity and variability of the backgrounds, the multi-scale, multi-type, and multi-posture diversity of ship targets.

  Methods  In order to promote the development of ship detection based on high-resolution optical remote sensing images (HRORSI), this paper provides an overview of existing study on ship detection methods of HRORSI over the past decades. Firstly, we introduce all the existing monitoring systems for ship detection, but subsequently focus only on HRORSI. Then, a number of topics have been discussed, including the methodology system, the development history, the recent state of the art detection methods, detection datasets and metrics.

  Results  It is shown that current ship detection methods based on deep learning in HRORSI, have greatly expanded the adaptability of the complexity of the detection scene and the variation of the target distribution, the speed and accuracy of the results have been greatly improved.It is also found that different influence factors make a big difference in choosing the corresponding ship detection models.

  Conclusions  Existing methods from different perspectives such as extracting richer features, multi-level and multi-scale feature fusion, more accurate target locating, scale aware ship detection, have made vigorous performance.However, there is still a big gap between efficient and intelligent ship interpretation in actual complex applications.We suggest that the future ship detection methods should adaptively support a variety of backgrounds, scales and optical sensors, the detection model can be effectively transfer to out-distributed target domain scenarios as well as resource-constrained scenarios, and more in-depth target recognition capabilities.